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Real-world evolution adapts robot morphology and control to hardware limitations

Authors:

Tønnes F. Nygaard,

Charles P. Martin,

Eivind Samuelsen,

Kyrre GletteAuthors Info & Claims

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference

Pages 125 - 132

https://doi.org/10.1145/3205455.3205567

Published: 02 July 2018 Publication History

Abstract

For robots to handle the numerous factors that can affect them in the real world, they must adapt to changes and unexpected events. Evolutionary robotics tries to solve some of these issues by automatically optimizing a robot for a specific environment. Most of the research in this field, however, uses simplified representations of the robotic system in software simulations. The large gap between performance in simulation and the real world makes it challenging to transfer the resulting robots to the real world. In this paper, we apply real world multi-objective evolutionary optimization to optimize both control and morphology of a four-legged mammal-inspired robot. We change the supply voltage of the system, reducing the available torque and speed of all joints, and study how this affects both the fitness, as well as the morphology and control of the solutions. In addition to demonstrating that this real-world evolutionary scheme for morphology and control is indeed feasible with relatively few evaluations, we show that evolution under the different hardware limitations results in comparable performance for low and moderate speeds, and that the search achieves this by adapting both the control and the morphology of the robot.

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Cited By

He ZCiocarlie M(2024)MORPH: Design Co-optimization with Reinforcement Learning via a Differentiable Hardware Model Proxy2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10610732(7764-7771)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10610732
Ferigo AIacca GMedvet EPigozzi F(2023)Evolving Hebbian Learning Rules in Voxel-Based Soft RobotsIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2022.322655615:3(1536-1546)Online publication date: Sep-2023
https://doi.org/10.1109/TCDS.2022.3226556
Le Goff LBuchanan EHart EEiben ALi WDe Carlo MWinfield AHale MWoolley RAngus MTimmis JTyrrell A(2023)Morpho Evolution With Learning Using a Controller Archive as an Inheritance MechanismIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2022.314854315:2(507-517)Online publication date: Jun-2023
https://doi.org/10.1109/TCDS.2022.3148543
Show More Cited By

Index Terms

Real-world evolution adapts robot morphology and control to hardware limitations
1. Computing methodologies
  1. Artificial intelligence
    1. Planning and scheduling
      1. Robotic planning
        Evolutionary robotics
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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Published In

cover image ACM Conferences

GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference

July 2018

1578 pages

ISBN:9781450356183

DOI:10.1145/3205455

Editor:
Hernan Aguirre
Shinshu University
,
General Chair:
Keiki Takadama
The University of Electro-Communications

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 July 2018

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Research Council of Norway

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GECCO '18

Sponsor:

SIGEVO

GECCO '18: Genetic and Evolutionary Computation Conference

July 15 - 19, 2018

Kyoto, Japan

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

He ZCiocarlie M(2024)MORPH: Design Co-optimization with Reinforcement Learning via a Differentiable Hardware Model Proxy2024 IEEE International Conference on Robotics and Automation (ICRA)10.1109/ICRA57147.2024.10610732(7764-7771)Online publication date: 13-May-2024
https://doi.org/10.1109/ICRA57147.2024.10610732
Ferigo AIacca GMedvet EPigozzi F(2023)Evolving Hebbian Learning Rules in Voxel-Based Soft RobotsIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2022.322655615:3(1536-1546)Online publication date: Sep-2023
https://doi.org/10.1109/TCDS.2022.3226556
Le Goff LBuchanan EHart EEiben ALi WDe Carlo MWinfield AHale MWoolley RAngus MTimmis JTyrrell A(2023)Morpho Evolution With Learning Using a Controller Archive as an Inheritance MechanismIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2022.314854315:2(507-517)Online publication date: Jun-2023
https://doi.org/10.1109/TCDS.2022.3148543
Bongard J(2023)From rigid to soft to biological robotsArtificial Life and Robotics10.1007/s10015-023-00872-028:2(282-286)Online publication date: 10-Apr-2023
https://dl.acm.org/doi/10.1007/s10015-023-00872-0
Medvet ENadizar GPigozzi FSalvato E(2023)Evolutionary Machine Learning in RoboticsHandbook of Evolutionary Machine Learning10.1007/978-981-99-3814-8_23(657-694)Online publication date: 2-Nov-2023
https://doi.org/10.1007/978-981-99-3814-8_23
Park HOh JCho YYun HHong HKang MPark KSong J(2022)Leg Structure based on Counterbalance Mechanism for Environmental Adaptive RobotJournal of the Korean Society of Manufacturing Process Engineers10.14775/ksmpe.2022.21.08.00921:8(9-18)Online publication date: 30-Aug-2022
https://doi.org/10.14775/ksmpe.2022.21.08.009
Howison THughes JIida F(2022)Morphological Sensitivity and Falling Behavior of Paper V-ShapesArtificial Life10.1162/artl_a_0034027:3–4(204-219)Online publication date: 16-Mar-2022
https://doi.org/10.1162/artl_a_00340
Nitschke GHoward D(2022)AutoFac: The Perpetual Robot MachineIEEE Transactions on Artificial Intelligence10.1109/TAI.2021.31047893:1(2-10)Online publication date: Feb-2022
https://doi.org/10.1109/TAI.2021.3104789
Jiang RDoshi NGondhalekar RRodriguez A(2022)Shape and Motion Optimization of Rigid Planar Effectors for Contact Trajectory Satisfaction2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS47612.2022.9981918(2628-2635)Online publication date: 23-Oct-2022
https://doi.org/10.1109/IROS47612.2022.9981918
Rebolledo MZeeuwe DBartz-Beielstein TEiben A(2022)Co-optimizing for task performance and energy efficiency in evolvable robotsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.104968113:COnline publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.engappai.2022.104968
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